The advent of powerful servers, large-scale data storage and other information infrastructure has spurred the development of advanced data warehousing and data mining applications. Structured query language (SQL) engines, on-line analytical processing (OLAP) databases and inexpensive large disk arrays have, for instance, been harnessed in financial, scientific, medical, and other fields to capture and analyze vast streams of transactional, experimental, and other data. The mining of that data can reveal sales trends, weather patterns, disease epidemiology and other patterns not evident from more limited or smaller-scale analysis.
In the case of health-related data management, the task of receiving, conditioning, and analyzing large quantities of clinical information in real-time is particularly challenging. The sources of health-related data for an organization to better provide context, for instance, include large data warehouses, such as statehealthfacts.org, data.gov, and clinical trials.gov, each of which may store many terabytes of data. The variety and depth of data represented in these data warehouses impedes the performance of typical querying strategies. Although there is widespread belief that data in these data warehouses can be used to inform health care and health at the personal and institutional levels, they are not structured to support real-time web access or implemented in a manner sufficiently robust to support health care delivery.